预测和理解艾滋病毒护理脱离的增强语言模型:坦桑尼亚的一个案例研究
Waverly Wei1, Junzhe Shao2, Rita Qiuran Lyu2
1Marshall School of Business, University of Southern California, Los Angeles, CA, USA.
NPJ digital medicine
|January 21, 2026
概括
一个增强的大型语言模型 (LLM) 准确地预测了那些有脱离艾滋病毒治疗的风险的人. 这种人工智能工具支持数据驱动的决策,以改善患者的治疗结果并减少艾滋病毒的传播.
科学领域:
- 人工智能在医学中的应用
- 公共卫生信息学 公共卫生信息学
- 艾滋病毒/艾滋病研究研究
背景情况:
- 持续参与艾滋病毒护理和抗逆转录病毒疗法 (ART) 的坚持对于实现全球艾滋病毒控制目标至关重要.
- 脱离艾滋病毒治疗是一个重大挑战,特别是在撒哈拉以南非洲,阻碍了治疗的有效性.
- 传统的机器学习 (ML) 模型在预测患者脱离参与方面表现出适度的成功,需要改进的预测工具.
研究的目的:
- 开发和评估一种增强的大型语言模型 (LLM),用于预测高风险脱离艾滋病毒护理的人.
- 利用电子医疗记录 (EMR) 来提高与传统的ML和零射击LLMs相比的预测准确性.
- 通过艾滋病毒医生的专家评估,评估LLM预测的临床相关性和实用性.
主要方法:
- 利用坦桑尼亚国家艾滋病毒护理和治疗计划 (2018-2023) 的48万个电子医疗记录 (EMR).
- 使用EMR数据开发和微调一个增强的大型语言模型 (LLM),以识别与ART不遵守,病毒载荷不抑制和随访损失相关的风险.
- 坦桑尼亚的艾滋病毒医生评估了LLM的预测和理由,以确定其临床相关性和与专家意见一致.
主要成果:
- 增强的LLM在预测艾滋病毒护理脱离方面表现出超越传统的ML模型和零射击LLM的潜力.
- 医生评估显示,LLM的预测与专家临床评估之间有65%的一致性.
- 在对齐的预测中,92.3%被认为具有临床意义,表明其实际效用.
结论:
- 增强的LLM显示承诺作为一种工具,用于识别患有脱离艾滋病毒护理风险的患者.
- 这种人工智能驱动的方法可以支持HIV管理中的数据驱动决策.
- 该模型有可能改善患者的治疗结果,提高治疗坚持度,并降低艾滋病毒传播率.
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